Demo Passing Data Between Components Parameters

• How to return a scalar value from one component.
• How to pass that scalar value as an input parameter to another component.
• Best practices and when to use parameters vs artifacts.

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Overview of the example

• train_model: performs (mock) training and returns a scalar accuracy (float).
• evaluate_model: receives the accuracy and an optional threshold and prints whether the model passed.

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Example implementation (KFP v2 DSL)

from kfp.v2 import compiler
from kfp.v2.dsl import component, pipeline

@component
def train_model() -> float:
    # Pretend training logic
    epochs = 10
    accuracy = 0.91 + (epochs * 0.001)  # 0.92
    return accuracy

@component
def evaluate_model(accuracy: float, threshold: float = 0.92):
    if accuracy >= threshold:
        print(f"Model passed with accuracy {accuracy}")
    else:
        print(f"Model failed with accuracy {accuracy}")

@pipeline(name="parameter-passing-example")
def ml_pipeline():
    train_task = train_model()
    # Pass the scalar output of train_task into evaluate_model
    evaluate_model(accuracy=train_task.output)

if __name__ == "__main__":
    # Compile the pipeline into a YAML file
    compiler.Compiler().compile(
        pipeline_func=ml_pipeline,
        package_path='passing-data-parameters.yaml'
    )

• Annotate train_model with -> float so the component output type matches the returned value.
• evaluate_model declares accuracy and an optional threshold (default 0.92).
• Inside the pipeline, capture the task returned by train_model() and pass train_task.output to evaluate_model(...).
• The compiler produces a pipeline specification YAML (passing-data-parameters.yaml) you can upload to the Kubeflow UI.

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Quick reference: component responsibilities

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Compile and run (high-level steps)

1. Save the Python script (e.g., pipeline.py) containing the example above.
2. Run the script locally to compile the pipeline:
   • python pipeline.py will generate passing-data-parameters.yaml.
3. Upload the YAML to the Kubeflow Pipelines UI:
   • In the UI, choose “Upload pipeline” → select passing-data-parameters.yaml → create a run.
4. Inspect the run once it finishes:
   • View each step’s Inputs / Outputs panels to verify the scalar parameter was passed correctly.

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What to expect in the UI

This step corresponds to execution "evaluate-model".

Input Parameters
accuracy 0.92

Output Artifacts
executor-logs
minio://mlpipeline/private-artifacts/...

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Best practices and guidance

• Use parameters for small scalar values (hyperparameters, thresholds, scalar metrics).
• Use artifacts to pass models, datasets, or other large/complex outputs.
• Always type your component outputs (e.g., -> float) so the compiler generates the correct component schema.

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Useful links and references

• Kubeflow Pipelines documentation: https://www.kubeflow.org/docs/components/pipelines/
• KFP SDK / DSL reference: https://www.kubeflow.org/docs/components/pipelines/sdk/overview/
• Compiler docs: https://github.com/kubeflow/pipelines/tree/master/sdk/python/kfp/v2